DSL line optimization is a sophisticated process that involves tuning various physical layer configurations such as data transmission rate, noise protection, operating margin etc., possibly over multiple closed-loop iterations. Uncertainties in the outcome of this process arise from the presence of hard-to-predict impulse noise in the channel and the fact that each DSL line can exhibit unique underlying characteristics such as interference, attenuation, etc. Despite these challenges, it is crucial to predict the outcome of DSL optimization so that, for example, appropriate resources can be efficiently allocated in the DSL network. For instance, a line whose performance improvement post-optimization is predicted to be significantly better than that of another line could be given higher priority in sending to a DSL optimizer to optimize, for example, if there is a limit on how many lines can be optimized at a, or over a particular period of, time.
Prior art predictions for DSL optimization performance is limited to capturing simple, plain, or absolute, conditions such as when system-level limits are violated (e.g., if desired post-optimization rate is above the channel capacity), or when the desired direction of optimization is not viable (e.g., if a DSL line is unstable and desired post-optimization rate is above current transmission rate, since it is deemed impossible to improve both rate and stability at the same time). In summary, prior art attempts at predicting DSL optimization performance may be more accurate in capturing low probability, edge-of-the-spectrum events, than in providing comprehensive performance quantification of the DSL optimizer for more common “middle-of-the-spectrum” events.